learning_rate: 0.0001
train_batch_size: 32
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Bleu 1 Msl	Bleu 2 Msl	Bleu 3 Msl	Bleu 4 Msl	Ter Msl	Bleu 1 Asl	Bleu 2 Asl	Bleu 3 Asl	Bleu 4 Asl	Ter Asl
No log	1.0	300	0.2151	0.0031	0.7679	0.6803	0.6089	0.5167	24.0921	0.9615	0.9355	0.9075	0.8756	4.8123
0.4301	2.0	600	0.1845	0.0031	0.8353	0.7778	0.7160	0.6210	18.5120	0.9768	0.9595	0.9390	0.9149	2.8975
0.4301	3.0	900	0.1652	0.0031	0.7743	0.7037	0.6406	0.5480	18.1577	0.9753	0.9574	0.9363	0.9106	3.0990
0.0922	4.0	1200	0.1778	0.0031	0.8149	0.7637	0.7139	0.6257	16.2976	0.9768	0.9595	0.9393	0.9151	2.8975
0.0573	5.0	1500	0.1854	0.0031	0.8296	0.7657	0.7028	0.6154	17.6262	0.9798	0.9641	0.9452	0.9220	2.4943
0.0573	6.0	1800	0.2056	0.0031	0.8118	0.7316	0.6512	0.5375	20.6377	0.9798	0.9642	0.9454	0.9226	2.3684
0.0376	7.0	2100	0.1904	0.0031	0.8111	0.7447	0.6831	0.5942	18.9548	0.9819	0.9667	0.9473	0.9235	2.2676
0.0376	8.0	2400	0.1995	0.0031	0.7990	0.7323	0.6681	0.5774	18.2462	0.9837	0.9699	0.9526	0.9311	1.9400
0.0313	9.0	2700	0.1854	0.0031	0.7930	0.7317	0.6723	0.5888	20.5492	0.9830	0.9687	0.9509	0.9286	1.9652
0.0211	10.0	3000	0.1965	0.0031	0.7923	0.7288	0.6682	0.5762	18.6005	0.9832	0.9694	0.9519	0.9303	1.9652
0.0211	11.0	3300	0.1845	0.0031	0.7928	0.7263	0.6630	0.5748	18.8663	0.9835	0.9694	0.9514	0.9292	1.9652
0.0185	12.0	3600	0.2036	0.0031	0.8338	0.7701	0.7111	0.6219	17.1833	0.9822	0.9686	0.9513	0.9295	2.0408
0.0185	13.0	3900	0.2222	0.0031	0.8072	0.7467	0.6880	0.5971	18.6891	0.9852	0.9719	0.9549	0.9338	1.8141
0.0134	14.0	4200	0.2190	0.0031	0.7826	0.7010	0.6305	0.5388	22.4978	0.9847	0.9716	0.9548	0.9339	1.8644
0.0097	15.0	4500	0.2151	0.0031	0.8007	0.7261	0.6617	0.5711	20.0177	0.9843	0.9707	0.9536	0.9321	1.8644
0.0097	16.0	4800	0.2083	0.0031	0.8034	0.7422	0.6843	0.6028	17.4491	0.9845	0.9715	0.9542	0.9324	1.8896
0.0074	17.0	5100	0.2247	0.0031	0.7831	0.7168	0.6572	0.5693	20.2834	0.9841	0.9708	0.9538	0.9326	1.9904
0.0074	18.0	5400	0.2348	0.0031	0.8080	0.7415	0.6793	0.5904	19.3091	0.9839	0.9702	0.9529	0.9310	1.9652
0.007	19.0	5700	0.2282	0.0031	0.8198	0.7650	0.7129	0.6271	19.3091	0.9841	0.9708	0.9535	0.9318	1.9400
0.0054	20.0	6000	0.2363	0.0031	0.7859	0.7273	0.6733	0.5908	20.8149	0.9854	0.9724	0.9554	0.9341	1.8141
0.0054	21.0	6300	0.2313	0.0031	0.8296	0.7676	0.7082	0.6170	18.2462	0.9860	0.9737	0.9574	0.9368	1.6881
0.0044	22.0	6600	0.2352	0.0031	0.7974	0.7346	0.6805	0.5993	19.3977	0.9843	0.9712	0.9542	0.9329	1.8141
0.0044	23.0	6900	0.2402	0.0031	0.7982	0.7360	0.6793	0.5948	19.3091	0.9845	0.9712	0.9540	0.9323	1.8393
0.0041	24.0	7200	0.2345	0.0031	0.8148	0.7558	0.7029	0.6164	18.6005	0.9852	0.9725	0.9557	0.9346	1.7889
0.0029	25.0	7500	0.2363	0.0031	0.8262	0.7659	0.7112	0.6293	17.2719	0.9843	0.9711	0.9541	0.9329	1.8896
0.0029	26.0	7800	0.2451	0.0031	0.8177	0.7535	0.6980	0.6197	17.4491	0.9841	0.9710	0.9540	0.9326	1.8644
0.0028	27.0	8100	0.2483	0.0031	0.8246	0.7628	0.7090	0.6236	17.8034	0.9854	0.9727	0.9563	0.9356	1.7637
0.0028	28.0	8400	0.2459	0.0031	0.8212	0.7609	0.7084	0.6276	17.8034	0.9845	0.9715	0.9546	0.9334	1.8393
0.0021	29.0	8700	0.2491	0.0031	0.8239	0.7626	0.7103	0.6280	17.8034	0.9850	0.9720	0.9551	0.9340	1.8141
0.002	30.0	9000	0.2524	0.0031	0.8270	0.7666	0.7145	0.6331	17.6262	0.9850	0.9720	0.9551	0.9340	1.8141

Framework versions

Transformers 4.51.1
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

vania2911/barto_exp1_10partition_modelo_msl12000

barto_exp1_10partition_modelo_msl12000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vania2911/
barto_exp1_10partition_modelo_msl12000